Integration of multiple data

sources into a resource

estimate – analysis of the

options

Geovariances Geostats RendezvousPerth February 26-27, 2013

Presented by Alastair CornahQuantitative Group, Fremantle

ac@qgroup.net.au

Introduction

�Multiple overlapping sources of ‘hard’ data in

brownfields projects.

�For example diamond, sonic, reverse circulation, and percussion drilling.

�Channel samples, blasthole samples

�How should these various data sources be

handled in resource estimation?

�Support?

�Precision?

�Bias?

Presented by :

Alastair Cornah

Treatment of lower precision or biased data in resource estimation

� Does including the lower precision (or biased) data help to

minimise estimation error?

� Additional data reduces the information effect, but is this

outweighed by increased estimation error as a result of

that data’s poor precision (or bias)?

� The answer is partly dependent upon the choice of

estimation method.

� Various geostatistical approaches exist which can be

used to maximise the value of low precision (or biased)

data in a resource estimate.

Presented by :

Alastair Cornah

Testwork

� Generation of a ‘ground truth’ within a two dimensional test area using conditional simulation of diamond drillhole data.

� Extraction of seven channel sample datasets

� Uniform error distributions applied to channel locations to iregularise the sampling pattern, avoid colocation of channels and drillholes.

� Gaussian error distributions (unbiassed) with increasing varianceapplied to extracted grades.

� Various estimation methods trailed using the drillholes and the various channel sample datasets (including and excluding channel samples).

� Estimations compared against the ground truth.

Presented by :

Alastair Cornah

Estimation options evaluated

� Estimation of drillholes only (ignoring channels) using Ordinary Kriging

� Integration of drillholes and channels estimation using Ordinary Kriging

� Integration of drillholes and channels, estimation using Cokriging

� Integration of drillholes and channels, estimation using ColocatedCokriging

� Integration of drillholes and channels, estimation using Ordinary Kriging with Variance of Measurement Error

Presented by :

Alastair Cornah

Conditional Simulation of Ground Truth

Presented by :

Alastair Cornah

Extraction of ‘virtual’ channel samples

Presented by :

Alastair Cornah

Estimated Fe (baseline) vs Ground Truth

Presented by :

Alastair Cornah

OK – drillholes only (baseline)

Presented by :

Alastair Cornah

OK – drillholes only (baseline)

Presented by :

Alastair Cornah

OK – drillholes and channels

Presented by :

Alastair Cornah

OK – drillholes and channels

Presented by :

Alastair Cornah

Cokriging – drillholes and channels

Presented by :

Alastair Cornah

Cokriging – drillholes and channels

Presented by :

Alastair Cornah

Colocated cokriging – drillholes and channels

Presented by :

Alastair Cornah

Colocated cokriging – drillholes and channels

Presented by :

Alastair Cornah

Ordinary Kriging with Variance of Measurement Error

Ordinary Kriging (channels & drillholes)Ordinary Kriging with Variance of

Measurement Error (channels & drillholes)

ChannelDrillhole

ChannelDrillhole

Presented by :

Alastair Cornah

Ordinary Kriging with Variance of Measurement Error – drillholes and

channels

Presented by :

Alastair Cornah

Ordinary Kriging with Variance of Measurement Error – drillholes and

channels

Presented by :

Alastair Cornah

OKVME – analysis of weights

Presented by :

Alastair Cornah

Conclusions under the testwork assumptions

� If OK estimation is used, channel samples influence the estimation significantly more than drillholes.

� In spite of this, benefit is gained by including the channels, as long as less than 2SD of sampling / measurement errors associated with the channels; beyond this using drillholes only is preferable.

� If CK, CCK, OKVME estimation is used, benefit is gained by incorporating the channels regardless of the level of sampling and measurement error (upto 3SD which was tested)

� OKVME rebalances kriging weights from channels to drillholes, depending upon the sampling / measurement error variance associated with the channels. Where channels contain any (unbiassed) error distribution it outperforms CK, CCK and OK.

Presented by :

Alastair Cornah